Intrusion Detection System (IDS) has increasingly become a crucial issue forcomputer and network systems. Optimizing performance of IDS becomes animportant open problem which receives more and more attention from the researchcommunity. In this work, A multi-layer intrusion detection model is designedand developed to achieve high efficiency and improve the detection andclassification rate accuracy .we effectively apply Machine learning techniques(C5 decision tree, Multilayer Perceptron neural network and Na"ive Bayes)using gain ratio for selecting the best features for each layer as to usesmaller storage space and get higher Intrusion detection performance. Ourexperimental results showed that the proposed multi-layer model using C5decision tree achieves higher classification rate accuracy, using featureselection by Gain Ratio, and less false alarm rate than MLP and na"ive Bayes.Using Gain Ratio enhances the accuracy of U2R and R2L for the three machinelearning techniques (C5, MLP and Na"ive Bayes) significantly. MLP has highclassification rate when using the whole 41 features in Dos and Probe layers.
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